遗传神经网络的瞬变电磁视电阻率求解算法

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Abstract The genetic neural network is applied to compute the central-loop transient electromagnetic (TEM) apparent resistivity. According to the central loop TEM response, a relationship between inputs and outputs of an ANN as well as three-layer architecture with single input and single output for ANN is designed. The ANN sample sets are calculated, and the number of hidden layer neuron is determined through trial method. A genetic algorithm is introduced to optimize connection weights of the ANN and then a GABP neural network with the optimal connection weights. A nonlinear equation of the TEM response is fitted by GABP neural network, and transient parameter value is obtained. The transient parameter value and kernel value from measured data are corresponding one by one. Finally, the apparent resistivity is calculated. Two examples with GABP neural network are presented. The first one is a forward calculated model of high impedance abnormal body in uniform half space, the second one is the grounding grid with the structural shape ‘?’ in a substation of power system. Pseudo-section of apparent resistivity is obtained, and good effects to solve inverse problem is achieved. The comparisons between the theoretical models and the measured data show that the GABP is a usefulness algorithm, which can reduce much computing time of TEM apparent resistivity. This method provides the base technology for a real-time fault diagnosis platform of grounding grids using TEM.

Key words： Transient electromagnetic field artificial neural networks genetic algorithm backpropagation apparent resistivity

Received: 14 October 2015 Published: 30 June 2017

PACS:

TM153.1

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	Qin Shanqiang
	Fu Zhihong
	Zhu Xuegui
	Ji Yongliang

Cite this article:

Qin Shanqiang,Fu Zhihong,Zhu Xuegui等. Genetic Neural Network for Apparent Resistivity Solution of Transient Electromagnetic[J]. Transactions of China Electrotechnical Society, 2017, 32(12): 146-154.

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https://dgjsxb.ces-transaction.com/EN/ OR https://dgjsxb.ces-transaction.com/EN/Y2017/V32/I12/146

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